SoroushVahidi/scidocs huggingface dataset:
Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies
(2017)

Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.
Mendeley readership counts: An investigation of temporal and disciplinary differences
(2016)

Mike Thelwall, Pardeep Sud Scientists and managers using citation-based indicators to help evaluate research cannot evaluate recent articles because of the time needed for citations to accrue. Reading occurs before citing, however, and so it makes sense to count readers rather than citations for recent publications. To assess this, Mendeley readers and citations were obtained for articles from 2004 to late 2014 in 5 broad categories (agriculture, business, decision science, pharmacy, and the social sciences) and 50 subcategories. In these areas, citation counts tended to increase with every extra year since publication, and readership counts tended to increase faster initially but then stabilise after about 5 years. The correlation between citations and readers was also higher for longer time periods, stabilising after about five years. Although there were substantial differences between broad fields and smaller differences between subfields, the results confirm the value of Mendeley reader counts as early scientific impact indicators.
To buy or not to buy online: adopters and non-adopters of online shopping in Singapore
(2006)

The Internet, as a dynamic virtual medium for selling and buying information, services and products, is gaining increasing attention from researchers and practitioners. In this study, we examine the perceptions of adopters and non-adopters of online shopping in terms of demographic profile, consumer expectations of online stores, advantages and problems of online shopping and transaction cost. In addition, we also examine the types of products purchased, frequency of online purchase and the extent of communication with e-commerce vendors. The findings are useful in explaining consumers’ buying behaviour in the electronic marketplace. Implications of the results are discussed.
When Ignorance is Bliss : Information , Fairness , and Bargaining Efficiency
(2003)

Most theories of legal discovery assume that sharing information among disputing parties will lead to convergence of expectations and will facilitate settlement. However, psychological research shows that shared information, if it is open to multiple interpretations, is likely to be interpreted egocentrically by the disputants, which can cause beliefs to diverge rather than converge. We present results from a bargaining experiment which shows that information sharing leads to divergence of expectations, and to settlement delays, when the information exchanged is amenable to multiple interpretations. By contrast, when there is only one obvious interpretation, information sharing leads to convergence of expectations and speeds settlement. We show, further, that information sharing moderates the relationship between size of the bargaining zone and the prospects for settlement. Information and efficiency 3 When Ignorance is Bliss: Information, Fairness, and Bargaining Efficiency
GSM Based Home Automation System UsingApp-Inventor for Android Mobile Phone
(2014)

Nowadays, the remote Home Automation turns out to be more and more significant and appealing. It improves the value of our lives by automating various electrical appliances or instruments. This paper describes GSM (Global System Messaging) based secured device control system using App Inventor for Android mobile phones. App Inventor is a latest visual programming platform for developing mobile applications for Android-based smart phones. The Android Mobile Phone Platform becomes more and more popular among software developers, because of its powerful capabilities and open architecture. It is a fantastic platform for the real world interface control, as it offers an ample of resources and already incorporates a lot of sensors. No need to write programming codes to develop apps in the App Inventor, instead it provides visual design interface as the way the apps looks and use blocks of interlocking components to control the app’s behaviour. The App Inventor aims to make programming enjoyable and accessible to novices.
BitIodine: Extracting Intelligence from the Bitcoin Network
(2014)

Bitcoin, the famous peer-to-peer, decentralized electronic currency system, allows users to benefit from pseudonymity, by generating an arbitrary number of aliases (or addresses) to move funds. However, the complete history of all transactions ever performed, called “blockchain”, is public and replicated on each node. The data it contains is difficult to analyze manually, but can yield a high number of relevant information. In this paper we present a modular framework, BitIodine, which parses the blockchain, clusters addresses that are likely to belong to a same user or group of users, classifies such users and labels them, and finally visualizes complex information extracted from the Bitcoin network. BitIodine labels users semi-automatically with information on their identity and actions which is automatically scraped from openly available information sources. BitIodine also supports manual investigation by finding paths and reverse paths between addresses or users. We tested BitIodine on several real-world use cases, identified an address likely to belong to the encrypted Silk Road cold wallet, or investigated the CryptoLocker ransomware and accurately quantified the number of ransoms paid, as well as information about the victims. We release a prototype of BitIodine as a library for building Bitcoin forensic analysis tools.
Evaluating User Privacy in Bitcoin
(2012)

Bitcoin is quickly emerging as a popular digital payment system. However, in spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place are publicly announced in the system. In this paper, we investigate the privacy provisions in Bitcoin when it is used as a primary currency to support the daily transactions of individuals in a university setting. More specifically, we evaluate the privacy that is provided by Bitcoin (i) by analyzing the genuine Bitcoin system and (ii) through a simulator that faithfully mimics the use of Bitcoin within a university. In this setting, our results show that the profiles of almost 40% of the users can be, to a large extent, recovered even when users adopt privacy measures recommended by Bitcoin. To the best of our knowledge, this is the first work that comprehensively analyzes, and evaluates the privacy implications of Bitcoin.
Secure and anonymous decentralized Bitcoin mixing
(2018)

The decentralized digital currency Bitcoin presents an anonymous alternative to the centralized banking system and indeed enjoys widespread and increasing adoption. Recent works, however, show how users can be reidentified and their payments linked based on Bitcoin’s most central element, the blockchain, a public ledger of all transactions. Thus, many regard Bitcoin’s central promise of financial privacy as broken. In this paper, we propose CoinParty, an efficient decentralized mixing service that allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. CoinParty, through a novel combination of decryption mixnets with threshold signatures, takes a unique place in the design space of mixing services, combining the advantages of previously proposed centralized and decentralized mixing services in one system. Our prototype implementation of CoinParty scales to large numbers of users and achieves anonymity sets by orders of magnitude higher than related work as we quantify by analyzing transactions in the actual Bitcoin blockchain. CoinParty can easily be deployed by any individual group of users, i.e., independent of any third parties, or provided as a commercial or voluntary service, e.g., as a community service by privacy-aware organizations.
Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm
(2010)

Bicycles that share the road with intelligent vehicles present particular challenges for automated perception systems. Bicycle detection is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint, bicycle detection is challenging as bicycle's appearance can change dramatically between viewpoints and a person riding on the bicycle is a non-rigid object. In this paper, we present a vision-based framework to detect and track bicycles that takes into account these issues. A mixture model of multiple viewpoints is defined and trained via a Support Vector Machine (SVM) to detect bicycles under a variety of circumstances. Each component of the model uses a part-based representation and known geometric context is used to improve overall detection efficiency. An extended Kalman filter (EKF) is used to estimate the position and velocity of the bicycle in vehicle coordinates. We demonstrate the effectiveness of this approach through a series of experiments run on video data of moving bicycles captured from a vehicle-mounted camera
A model-free mapless navigation method for mobile robot using reinforcement learning
(2018)

In this paper, a simple and efficient method is proposed using one of the basic reinforcement learning tool Q-learning. This method is to achieve the aim of mobile robot maples navigation with collision-free. Thus, an end-to-end navigation model is built, which uses lidar sensor massage as input and moving command of mobile robot as output, so as to simplify the process of environmental perception and decision making on action. Through simulation and experiments, its effectiveness is proved. After trained by this method, mobile robot can safely reach the navigation target in an unknown environment without any prior demonstration. In addition, an extensive quantitative and qualitative evaluation of this method is presented by the comparison with traditional path planning method based on foregone global environment map.
Power Density Limits and Design Trends of High-Speed Permanent Magnet Synchronous Machines
(2015)

Electrical machines will always be a subcomponent in a larger system and should therefore be designed in conjunction with other major parts. Only a limited set of parameters, such as weight, size, or speed, is often relevant at a system level, but accurately determining those parameters requires performing a time-consuming detailed design of the electrical machine. This conflicts with a top down system design approach, where initially many different system topologies need to be considered. Hence, there is a need for a consistent and detailed description of the dependency of the interface parameters on machine properties. This paper proposes a method to obtain detailed design trends of electrical machines with a very high level of detail, based on large-scale application of multiobjective optimization using finite element models. The method is demonstrated by quantifying the power density of surface-mounted permanent magnet (SPM) machines versus rotor surface speed, power level, and cooling approach. It is found that power density strongly depends on cooling approach, followed by rotor surface speed, but barely on power level. Ultimately, this paper can be used both as an example when performing a similar design study and as a reference on detailed high-level and low-level machine design trends.
Long-term electroencephalogram measurement using polymer-based dry microneedle electrode
(2015)

This paper reports a successful electroencephalogram (EEG) measurement for hours using polymer-based microneedle electrodes. Needle electrodes can penetrate through the stratum corneum and therefore, do not require any skin treatment for high-quality EEG measurement. The tested needles consist of SU-8 needles, a silver film, and a nanoporous parylene protective film. In prior work, fabrication processes of polymer-based microneedles, which are considered to be more robust than silicon microneedles was developed. In this work, the electrical impedance was measured at the forehead and was verified to maintain 6 kΩ for 3 h without any skin treatment, which was low enough for EEG measurement. A headset was designed to keep the contact between the needles and skin and with its help, EEG was successfully measured from the frontal poles. The acquired signals were found to be as high quality as the standard wet electrode that required skin treatment and uncomfortable pasting of conductive gel. The developed electrodes are readily applicable to record brain activities for hours while applying little mental and physical stress to the users.
On Maximizing Sensor Network Lifetime by Energy Balancing
(2018)

Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.
Knowledge-based processing of complex stock market events
(2012)

Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event stream and background knowledge about companies which is stored in a knowledge base. Users of our system can express their queries in a rule language which provides functionalities to specify semantic queries about companies in the SPARQL query language for querying the external knowledge base and combine it with event data stream. Background makes it possible to detect stock market events based on companies attributes and not only based on syntactic processing of stock price and volume.
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation
(2016)

[3] Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Leichter, D.K.C.R.I., Wei, A.V.Y., Krupka, D.F.P.K.E., Fitzgibbon, A., Izadi, S.: Accurate, robust, and flexible real-time hand tracking. In: CHI (2015) [4] Oberweger, M., Wohlhart, P., Lepetit, V.: Training a feedback loop for hand pose estimation. In: ICCV (2015) [18] Oberweger, M., Wohlhart, P., Lepetit, V.: Hands deep in deep learning for hand pose estimation. arXiv preprint arXiv:1502.06807 (2015) [19] Tang, D., Taylor, J., Kohli, P., Keskin, C., Kim, T.K., Shotton, J.: Opening the black box: Hierarchical sampling optimization for estimating human hand pose. In: ICCV (2015) * Indicates equal contribution. Results T θ
Structured Sparse Method for Hyperspectral Unmixing
(2014)

Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly. Index Terms Hyperspectral Unmixing (HU), Hyperspectral Image Analysis, Structured Sparse NMF (SS-NMF), Mixed Pixel, Nonnegative Matrix Factorization (NMF).
Electronic energy transfer.
(2010)

COVER ARTICLE Fleming et al. Quantum coherence and its interplay with protein environments in photosynthetic electronic energy transfer PERSPECTIVE Albinsson and Mårtensson Excitation energy transfer in donor– bridge–acceptor systems Includes a collection of articles on the theme of electronic energy transfer Recent experiments suggest that electronic energy transfer in photosynthetic pigment-protein complexes involves long-lived quantum coherence among electronic excitations of pigments. The observation has led to the suggestion that quantum coherence might play a significant role in achieving the remarkable efficiency of photosynthetic light harvesting. At the same time, the observation has raised questions regarding the role of the surrounding protein in protecting the quantum coherence. In this Perspective, we provide an overview of recent experimental and theoretical investigations of photosynthetic electronic energy transfer paying particular attention to the underlying mechanisms of long-lived quantum coherence and its non-Markovian interplay with the protein environment.
Text-based domain ontology building using Tf-Idf and metric clusters techniques
(2007)

The paper describes the methodology used to develop a construction domain ontology, taking into account the wealth of existing semantic resources in the sector ranging from dictionaries to thesauri. Given the characteristics and settings of the construction industry, a modular, architecture-centric approach was adopted to structure and develop the ontology. The paper argues that taxonomies provide an ideal backbone for any ontology project. Therefore, a construction industry standard taxonomy was used to provide the seeds of the ontology, enriched and expanded with additional concepts extracted from large discipline-oriented document bases using information retrieval (IR) techniques.
A Survey on Parts of Speech Tagging for Indian Languages
(2017)

Part of speech (POS) tagging is basically the process of automatically assigning its lexical category to each word according to its context and definition. Each word of sentence is marked in croups as corresponding to a particular part of speech like noun, verb, adjective and adverb. POS serves as a first step in natural language process applications like information extraction, parsing, and word sense disambiguation etc. this paper presents a survey on Part of Speech taggers used for Indian languages. The main problem of tagging is to find proper way to tag each word according to particular part of speech. Very less work has been done for POS tagging on Indian languages mainly due to morphologically richness. In this paper, various techniques are discussed that are used for development of POS
SINGLE PHASE PWM RECTIFIER IN TRACTION APPLICATION
(2011)

This research has been motivated by industrial demand for single phase PWM rectifier in traction application. This paper presents an advanced control structure design for a single phase PWM rectifier. The PWM rectifier is controlled to consume nearly sinusoidal current with power factor nearing to unity. The control structure consists of a DC-link voltage controller and a current controller implemented by a proportionalresonant controller using a fast phase angle and frequency estimator. The estimation algorithm is derived from the weighted least-squares estimation method. The feasibility of the proposed control structure is confirmed by experimental tests performed on designed laboratory prototype.
System-Level Performance of Downlink Non-Orthogonal Multiple Access (NOMA) under Various Environments
(2015)

Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for further improving the spectrum efficiency compared to that for orthogonal multiple access (OMA) in the 5th Generation (5G) mobile communication systems. All of the existing evaluations for NOMA focus on the macrocell deployment since NOMA fully utilizes the power domain and the difference in channel gains, e.g., path loss, between users, which is typically sufficiently large in macrocells. Currently, small cells are becoming important and being studied for future Long-Term Evolution (LTE) enhancements in order to improve further the system performance. Thus, it is of great interest to study the performance of NOMA for small cell deployment under various environments. This paper investigates the system level performance of NOMA in small cells considering practical assumptions such as the single user multiple-input multiple-output (SU-MIMO) technique, adaptive modulation and coding (AMC), feedback channel quality indicator (CQI). Some of the key NOMA specific functionalities, including multi-user paring and transmit power allocation are also taken into account in the evaluation. Based on computer simulations, we show that for both macrocell and small cell deployments, NOMA can still provide a larger throughput performance gain compared to that for OMA.